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The LinkedIn Privacy Paradox “On
one hand, the company has 500+ million members trusting the company to protect highly sensitive data. On the other hand, one only joins the largest professional network on the Internet because they want to be found !" Kalinda Raina, Head of Global Privacy, LinkedIn MEMBER PRIVACY <> MEMBER DISCOVERY

metric scripts Members First is
a Core Value for LinkedIn MEMBER PRIVACY WHILE DELIVERING MEMBER VALUE production code Well-connected. Get relevance right. Few connections. Give them inventory. Example Member value is proportional to knowledge Member privacy is paramount for LinkedIn We strive to maintain this fine balance

Data Is the Lifeblood of
LinkedIn MEMBER EXPERIENCES + BUSINESS DECISIONS production code Member Data System of Intelligence Member Experiences Business Decisions

We needed data democracy to
deliver member value LinkedIn Data Science I want to analyze as much data as possible so my models are accurate Data Democracy ALL THE DATA, ALL THE TIME I want to discover data that’s needed for my analysis as fast as possible I want to access that data as quickly as possible for my analysis

I want my personal data
to be stored only where needed and not propagated unnecessarily Data Protection Need to Ensure Member Privacy LinkedIn Members STORE, PROCESS, DELETE,.. I want my personal data to be deleted when I close my account or request deletion I want my personal data to only be processed if essential and only if I consent

REQUIREMENTS Less Data Legal: Right
to Erasure or Right to be Forgotten “Delete all my personal data without undue delay when it is no longer necessary / when consent has been withdrawn” Engineering: Need the ability to delete some specific subset or all data associated with a specific LinkedIn member from all our data systems

More than just Discovery Use
Cases Which datasets at LinkedIn contain PII or highly confidential data? How many contain member-member messages? How many of them are accessible by team X? Have all datasets been purged within SLA? Discovering Violations ANSWERING HARDER QUESTIONS

Simple to Complex Different Types
Basic Restrictions Access to dataset based on business need Privacy by Default Analysts shouldn’t get access to raw PII by default Consent-based Access Access to certain data elements only available if member has consented for that particular use- case Access Restrictions REQUIREMENTS

Getting Stricter and more complex
Data Protection Key Takeaways THE BEAST IS REAL Stricter regulations in a digital world Increasingly more complex to implement This is an accelerating global trend

We’ve established a blueprint to
sustainably address privacy Learnings at LinkedIn Key Takeaways THE BEAST CAN BE TAMED ! Privacy By Design : baked into technology stack & product development process Standardization : To solve at scale, certain parts need to be centralized and standardized Company-wide : Needs co-ordinated effort across various functions